Turf management

ABSTRACT

A turf management system includes an irrigation system for providing water to the turf at a turf site, and an irrigation evaluation system for evaluating the performance of the irrigation system. The irrigation evaluation system segments the turf site into irrigation management units, where each of the irrigation management units includes at least one sprinkler head. Qualities of the turf are measured to generate collected data points. The data points are used by the irrigation evaluation system to compute a value for the turf in each of the irrigation management units. The values can then be used to identify irrigation management units having common characteristics. The values can be used to define irrigation management zones for controlling the irrigation system.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.17/451,765, filed on Oct. 21, 2021, which is a continuation of U.S.application Ser. No. 15/841,011, filed on Dec. 13, 2017, issued as U.S.Pat. No. 11,178,829 on Nov. 23, 2021, which is a continuation of U.S.patent application Ser. No. 12/895,339, filed on Sep. 30, 2010, issuedas U.S. Pat. No. 9,872,445 on Jan. 23, 2018, all the above-entitled TURFMANAGEMENT, the disclosures of which are hereby incorporated byreference in their entireties. To the extent appropriate, a claim ofpriority is made to each of the above-disclosed applications.

TECHNICAL FIELD

The present disclosure relates to turf management, and more particularlyto evaluation and control of a turf irrigation system to improve theirrigation system's performance.

BACKGROUND

Turf is commonly used as a ground covering for a variety of recreationaland non-recreational purposes. Because turf often needs more water thanis naturally available, irrigation systems can be installed in or aroundthe turf to provide additional water to the turf as needed. However, itcan be difficult to determine when water is needed, how much water isneeded, and how best to apply that water. Moreover, a variety of factorscan influence the needs of the turf grass at any given location. Forexample, the turf grass may be growing over several different types ofsoil; some types, such as clay, tending to hold moisture, while othertypes, such as sand, tending to allow the moisture to quickly soakthrough. To account for such variances, it is often necessary toover-water some portions of the turf in order to provide adequateamounts of water to other portions of the turf. Over-watering, however,can be detrimental to the turf, resulting in poor turf conditions atthose locations. The operation of an irrigation system in this way isinefficient and leads to unnecessary cost of operation.

SUMMARY

In general terms, this disclosure is directed to turf management. In onepossible configuration and by non-limiting example, the disclosurerelates to evaluation and control of a turf irrigation system to improvethe irrigation system's performance.

One aspect is a method of evaluating an irrigation system at a turfsite, the turf site including turf. The method includes segmenting theturf site into a plurality of irrigation management units, wherein eachirrigation management unit of the turf site includes at least onesprinkler head; and for each irrigation management unit, computing witha computing device a value representing a characteristic of the turfwithin the irrigation management unit.

Another aspect is an irrigation system for providing water to turf at aturf site. The irrigation system includes sprinkler heads, water lines,and a control system. The water lines are connected to a source of waterand to valves that control the flow of water through the sprinklerheads. The control system includes a computing device. The computingdevice is operably connected to the valves to selectively open thevalves to allow water to flow through the sprinkler heads and onto theturf. The computing device is programmed to commonly control a firstplurality of the sprinkler heads within a first irrigation managementzone according to a first set of control parameters, and is programmedto commonly control a second plurality of sprinkler heads within asecond irrigation management zone according to a second set of controlparameters. The first plurality of sprinkler heads are all positioned inturf having a first common characteristic, and the second plurality ofsprinkler heads are all positioned in turf having a second commoncharacteristic.

A further aspect is a method of evaluating and controlling an irrigationsystem at a turf site including turf. The method including transportinga data collection vehicle to a turf site, the data collection vehicleincluding at least a location identification device and at least oneinstrument adapted to measure a quality of the turf; collecting datapoints using the data collection vehicle, by moving the data collectionvehicle across the turf while operating the instrument, the data pointsincluding a value indicative of the measured quality of the turf and alocation where the data point was obtained; identifying areas of theturf site having similar qualities using the data points; and definingirrigation management zones, wherein each irrigation management zoneincludes areas of the turf site having similar qualities.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an example turf management system formanagement of a turf site.

FIG. 2 is a schematic block diagram of a portion of an exampleirrigation system of the turf management system shown in FIG. 1 .

FIG. 3 is a schematic block diagram illustrating an example computingdevice of the turf management system shown in FIG. 1 .

FIG. 4 is a perspective side view of an example mobile data collectiondevice of the turf management system shown in FIG. 1 .

FIG. 5 is a flow chart illustrating an example method of evaluating aturf site.

FIG. 6 is a schematic diagram illustrating the collection of data fromthe turf site.

FIG. 7 is a graphical illustration of the data collected in FIG. 6 .

FIG. 8 is a schematic block diagram of an example data processing lab ofthe turf management system shown in FIG. 1 .

FIG. 9 is a schematic block diagram of an example irrigation managementzoning module of the turf management system shown in FIG. 1 .

FIG. 10 is a graphical representation of interpolated data as computedby the turf management system shown in FIG. 1 .

FIG. 11 is a graphical representation of the interpolated data of FIG.10 .

FIG. 12 is a graphical representation of operations performed by anirrigation management unit engine of the turf management system shown inFIG. 1 .

FIG. 13 is a graphical representation of additional operations performedby the irrigation management unit engine of the turf management systemshown in FIG. 1 .

FIG. 14 is a diagram illustrating the classification of irrigationmanagement units into irrigation management zones by turf managementsystem shown in FIG. 1 .

FIG. 15 illustrates the classification of irrigation management unitsinto irrigation management zones with the turf management system shownin FIG. 1 .

FIG. 16 is a schematic block diagram of a fine tuning module of the turfmanagement system shown in FIG. 1 .

FIG. 17 is a graphical representation of operations performed by asalinity processing engine of the turf management system shown in FIG. 1.

FIG. 18 is a schematic block diagram of a topography processing engineof the turf management system shown in FIG. 1 .

FIG. 19 is a chart illustrating the operation of a steepness engine ofthe turf management system shown in FIG. 1 .

FIG. 20 is a diagram illustrating the operation of an aspect engine ofthe turf management system shown in FIG. 1 .

FIG. 21 is a diagram illustrating operations of the topographyprocessing engine shown in FIG. 18 .

DETAILED DESCRIPTION

Various embodiments will be described in detail with reference to thedrawings, wherein like reference numerals represent like parts andassemblies throughout the several views. Reference to variousembodiments does not limit the scope of the claims attached hereto.Additionally, any examples set forth in this specification are notintended to be limiting and merely set forth some of the many possibleembodiments for the appended claims.

FIG. 1 is a schematic diagram of an example turf management system 100for management of a turf site 101. In this example, turf managementsystem 100 includes an irrigation system 102 and an irrigationevaluation system 104.

Turf site 101 includes turf 103 to be managed by turf management system100. As discussed above, turf sites can take a variety of recreationaland non-recreational forms. By way of example, turf site 101 isdescribed herein as a golf course, including fairways 105, 107, and 109.However, a variety of other types of turf sites can alternatively bemanaged by turf management system 100, such as sporting fields includingbaseball, soccer, football fields; turf race tracks; park lawns;residential or commercial lawns; or any of a variety of other turf sitesthat include turf 103. Further, turf management system 100 is notlimited to fairways, and can further be used in the management ofgreens, tees, roughs, and surrounding lawns of the golf course in someembodiments.

Irrigation system 102 provides water to turf 103 of turf site 101.Various types of irrigation systems 102 can be used in the many possibleembodiments. For example, in some embodiments irrigation system 100includes irrigation control system 120 (such as including primarycontrol system 122 and satellite control systems 124), pump station 126,water delivery pipes 128, control lines 130, and sprinkler heads 132. Anexample of irrigation system 102 is illustrated and described in moredetail with reference to FIG. 2 .

The term “turf” is sometimes used herein to refer to the entire groundlayer including a ground covering composed of vegetation, such as grass,and the ground or soil located below the ground covering, which supportsthe growth of the ground covering. The present disclosure can also beused with other ground coverings, other than grass.

Irrigation evaluation system 104 is a system that evaluates theperformance of irrigation system 102, such as to identify ways that theirrigation system 102 can be improved. In some embodiments theirrigation evaluation system 104 further operates to define controlparameters for the irrigation system 102. As shown in FIG. 2 , someembodiments of irrigation evaluation system 104 include a mobile datacollection device 140 and data processing lab 142.

In an example embodiment, the mobile data collection device 140 is usedto collect various data about the turf 103 at turf site 101. The mobiledata collection device 140 includes a plurality of sensors that measurequalities of the turf 103 at different locations as the mobile datacollection device 140 is driven across the turf 103. An example ofmobile data collection device 140 is described in more detail withreference to FIG. 4 .

Data processing lab 142 receives the data collected by mobile datacollection device 140, and processes the data. In some embodiments, theresult of the data processing is the identification of physicaladjustments that can be made to the irrigation system 102 to improve theperformance of the irrigation system 102. For example, the results mayindicate that two sprinkler heads that are currently operated as a pairshould be separately controlled. As another example, the results mayindicate where in-ground moisture sensors should be installed.Alternatively, or in addition, the data processing performed by the dataprocessing lab provides information to identify adjustments that can bemade to the operation of the irrigation system 102 to improve theperformance of the irrigation system 102. For example, in someembodiments the data processing identifies irrigation management zones,which are regions of the turf site 101 that have similar qualities, suchas similar watering requirements. The irrigation control system 120 canthen be programmed to water each irrigation management zone according tothe unique needs of the turf 101 in that zone. Other embodiments provideresults and perform different data processing operations, as discussedherein. An example of the data processing lab 142 is illustrated anddescribe in more detail with reference to FIG. 8 .

FIG. 2 is a schematic block diagram of a portion of an exampleirrigation system 102 at a turf site 101. In this example, turf site 101is a golf course fairway 105 having turf 103 that is managed byirrigation system 102. The irrigation system 102 includes control system120, a pump station 126, water delivery pipes 128, control lines 130,and sprinkler heads 132.

In this example, control system 120 includes a primary control system122 and one or more satellite control systems 124. The primary controlsystem 122 is, for example, a computing device that is centrallylocated, such as in the office of the golf course superintendent. Thesuperintendent can use the primary control system 122 to monitor andcontrol the operation of the irrigation system 102. The primary controlsystem 122, is also a computing device, for example, which communicateswith a plurality of satellite control systems 124. The primary controlsystem 122 can coordinate and control the operation of the satellitecontrol systems 124, which are distributed around the golf course. Forexample, in some embodiments each fairway has a separate satellitecontrol system 124, or even multiple satellite control systems 124. Insome embodiments, however, primary control system 122 is capable ofcontrolling the irrigation system 102, without the need for additionalsatellite control systems 124. Other types and configurations of controlsystems can be used in other embodiments. An example of a computingdevice of the primary control system 122 is illustrated and describedwith reference to FIG. 3 .

In some embodiments, control system 120 is connected to a datacommunication network 150, such as a local area network or the Internet.The communication network 150 can be used for communication withinirrigation system 102 (such as between primary control system 122,satellite control systems 124, and sprinkler heads 132). In anotherpossible embodiment, communication network 150 is used for communicationwith a remote computing device 152. One example of a remote computingdevice 152 is a computer utilized by the superintendent to monitor andcontrol irrigation system 102 remotely. Another example of remotecomputing device 152 is a computer at the data processing lab, which canbe used to send data or information to the superintendent or directly tothe irrigation system 102. For example, in some embodiments the remotecomputing device 152 sends updated control parameters directly from thedata processing lab 142 to irrigation system 102 to improve theoperation of the irrigation system 102.

Pump station 126 receives water from a water source, and delivers thewater to water delivery pipes 128 for distribution to sprinkler heads132. The water source is any suitable water source, such as a city orcommunity water supply, or a natural water source, such as a well,spring, lake, or river. The water source may include effluent orrecycled water. Water from the water source is pressurized by the pumpstation 126 and the pressurized water is then output to water deliverypipes 128, which are typically buried in the ground. The water deliverypipes 128 are connected to each sprinkler head 132.

Control lines 130 are used, in some embodiments, for communicationbetween the primary control system 122 and satellite control systems124, as well as to individual sprinkler heads 132. In some embodimentscontrol lines 130 are electrical wires through which digital messages(or other electrical signals) are communicated. For example, anindividual sprinkler head can be turned on by sending a message acrossthe control line 130 that includes a message including a unique addressof the sprinkler head and an instruction for that sprinkler head to turnon. In another possible embodiment, sprinkler heads are turned on byproviding power to the sprinkler head, and turned off by removing thepower to the sprinkler head. Other communication and control techniquesare used in other possible embodiments, such as wireless communicationand telephone line communication. Decoders are used in some embodiments.Further, in some embodiments handheld wireless devices are used tocontrol the irrigation system.

Other embodiments include other types of control lines, such ashydraulic lines. In a hydraulic system, for example, the control lines130 are small tubes that are filled with pressurized fluid. A signal canbe communicated through the tubes by adjusting the pressure of thefluid. For example, the sprinkler heads 132 are turned on by decreasingthe pressure in the fluid, and turned off by increasing the pressure inthe fluid.

Sprinkler heads 132 provide water from water delivery pipes 128 to theturf 103. Typically sprinkler heads 132 extend up through the turf andspray water across the upper surface of the turf 103. Sprinkler heads132 often include a valve enclosed within the casing of the sprinklerhead 132. The valve is controlled based on signals received throughcontrol lines 130 from satellite control system 124 or primary controlsystem 122. In another embodiment, however, valves are installed alongthe water delivery pipes 128, such as to permit simultaneous control ofmultiple sprinkler heads 132 with a single valve. Any of a variety ofsprinkler heads can be used in other embodiments.

Further, in some embodiments the irrigation system 102 includes dripirrigation lines. Drip irrigation lines can be used to precisely deliverwater to particular areas of turf 103, such as around edges of a sandtrap.

FIG. 3 is a schematic block diagram illustrating an example computingdevice 200 of the primary control system 122. Although described asbeing part of the primary control system 122, computing device 200 isalso an example of a computing device that can be used to perform one ormore of the methods, operations, computations, or processes discussedherein by other computing devices. For example, referring to FIG. 1 ,computing device 200 is an example of a computing device of satellitecontrol system 124, a computing device of pump station 126, a computingdevice of mobile data collection device 140, a computing device of dataprocessing lab 142, or a computing device of remote computing device 152(shown in FIG. 2 ). However, some of these computing devices do notinclude all of the components shown in FIG. 3 , such as the interfacewith irrigation system control lines 130. Because computing device 200is a suitable example of these other computing devices, they will not beseparately described herein.

In one example embodiment, computing device 200 is a personal computer.Other embodiments include other computing devices 200, such as a tabletcomputer, a smart phone, a personal digital assistant (PDA), or otherdevice configured to process data instructions. In some embodiments,computing device 200 is an example of programmable electronics. Inanother possible embodiment, two or more computing devices 200collectively form at least a portion of the programmable electronics.

Computing device 200 includes, in some embodiments, at least oneprocessing device 202 and memory 204. A variety of processing devices202 are available from a variety of manufacturers, for example, Intel orAdvanced Micro Devices. In some embodiments, the processing device 202is configured to perform one or more methods or operations as defined byinstructions stored in a memory device. Examples of such methods andoperations are described herein.

Computing device 200 also includes, in some embodiments, at least onememory device 204. Examples of memory devices 204 include read-onlymemory 208 and random access memory 210. Basic input/output system 212,containing the basic routines that act to transfer information withincomputing device 200, such as during start up, is typically stored inread-only memory 208. Memory device 204 can be a part of processingdevice 202 or can be separate from processing device 202.

In this example, computing device 200 also includes system bus 206 thatcouples various system components including memory 204 to processingdevice 202. System bus 206 is one of any number of types of busstructures including a memory bus, or memory controller; a peripheralbus; and a local bus using any of a variety of bus architectures.

In some embodiments, computing device 200 also includes secondarystorage device 214 for storing digital data. An example of a secondarystorage device is a hard disk drive. Secondary storage device 214 isconnected to system bus 206 by secondary storage interface 216.Secondary storage devices 214 and their associated computer readablemedia provide nonvolatile storage of computer readable instructions(including application programs and program modules), data structures,and other data for computing device 200.

Although the exemplary architecture described herein employs a hard diskdrive as a secondary storage device, other types of computer readablemedia are included in other embodiments. Examples of these other typesof computer readable media include magnetic cassettes, flash memorycards, digital video disks, Bernoulli cartridges, compact disc read onlymemories, digital versatile disk read only memories, random accessmemories, read only memories, or other memory devices.

A number of program modules can be stored in secondary storage device214 or memory 204, including operating system 218, one or moreapplication programs 220, other program modules 222, and program data224. In some embodiments, program modules include data instructions thatare stored in computer readable media (such as computer readable storagemedia). The data instructions, when executed by the processing device202, cause the processing device 202 to perform one or more of themethods or operations described herein. Program data 224 includes, forexample, control parameters for the irrigation system 102 that are usedby the control system 120 to define the operation of the irrigationsystem 102. For example, control parameters can include thresholdmoisture values used by the control system 120 to determine when watershould be supplied to a portion of the turf 103, frequency parametersthat define how frequently the water should be applied, and durationparameters that define how long water should be supplied in a singlewatering.

In some embodiments, a user, such as the golf course superintendent,provides inputs to the computing device 200 through one or more inputdevices 230. Examples of input devices 230 include keyboard 232, mouse234, touchpad 236, and touch sensitive display 238. Other embodimentsinclude other input devices 230. Input devices 230 are often connectedto the processing device 202 through input/output interface 240 that iscoupled to system bus 206. These input devices 230 can be connected byany number of input/output interfaces, such as a parallel port, serialport, game port, or a universal serial bus. Wireless communicationbetween input devices and interface 240 is possible as well, andincludes infrared, BLUETOOTH® wireless technology, 802.11a/b/g/n/zwireless communication, cellular communication, or other radio frequencycommunication systems in some possible embodiments.

In some embodiments, input output interface 240 is also coupled toirrigation system control lines 130, to communicate with satellitecontrol systems 124, or to communicate directly with sprinkler heads132.

In some embodiments, a display device 242, such as a monitor, liquidcrystal display device, projector, or touch screen display device, isconnected to system bus 206 via an interface, such as display adapter244. In addition to display device 242, the computing device 200 caninclude various other peripheral devices (not shown), such as speakersor a printer. In some embodiments the display device 242 and touchsensitive display 238 are the same device.

When used in a local area networking environment or a wide areanetworking environment (such as the Internet), computing device 200 istypically connected to network 252 through a network interface oradapter 250. Other possible embodiments use other communication devices.For example, some embodiments of computing device 200 include a modemfor communicating across network 252.

Computing device 200 typically includes at least some form ofcomputer-readable media. Computer readable media include any availablemedia that can be accessed by computing device 200. By way of example,computer-readable media include computer readable storage media andcommunication media.

The term computer readable media as used herein includes computerstorage media. Computer storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information, such as computer readableinstructions, data structures, program modules, or other data. Computerreadable storage media includes, but is not limited to, read-only memory208, random access memory 210, electrically erasable programmable readonly memory, flash memory or other memory technology, compact disc readonly memory, digital versatile disks or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store thedesired information and that can be accessed by computing device 200. Insome embodiments, computer readable storage media is non-transitorymedia.

Communication media can be embodied by computer readable instructions,data structures, program modules or other data in a modulated datasignal, such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in the signal. By way ofexample, communication media includes wired media such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,radio frequency, infrared, and other wireless media. In someembodiments, communication media is transitory media. Combinations ofany of the above are also included within the scope of computer readablemedia.

FIG. 4 is a perspective side view of an example mobile data collectiondevice 140 for collecting data about turf 103 at a turf site 101, suchas shown in FIG. 2 . In this example, mobile data collection device 140includes a data collection vehicle 280 and a mobile turf instrumentapparatus 282.

Data collection vehicle 280 is a motorized vehicle that propels themobile turf instrument apparatus 282. Although illustrated as a separatevehicle, in some embodiments the data collection vehicle 280 and mobileturf instrument apparatus 282 are a single device.

In this example, data collection vehicle 280 includes frame 290, body292, wheels 293, motor 294, power source 296, a trailer hitch 298, acomputing device 300, display device 302, and electrical wiring 304. Thedata collection vehicle 280 may powered by any of a variety of powersources 296, such as gasoline or electricity from a battery. Wheels 293are preferably of a larger width to distribute the weight of the datacollection vehicle about a larger area of turf 103 (shown in FIG. 2 ),to reduce compaction of the soil.

Trailer hitch 298 is connected to the frame 290 of data collectionvehicle 280 to permit a tongue of the mobile turf instrument apparatus282 to be removably coupled to the data collection vehicle 280.

In this example, a computing device 300 and display device 302 areprovided on data collection vehicle 280 to receive and store datacollected by mobile turf instrument apparatus 282. In some embodiments,the computing device 300 displays a graphical user interface on displaydevice 302, which displays a map of turf site 101. For example, the mapis annotated with lines indicating where the data collection vehicle 280has already gone, to assist the driver of the data collection vehicle280 in proceeding along the appropriate data collection path. In anotherpossible embodiment, the path is physically marked, such as with flags,foam markers, paint, chalk, or other markers.

Electrical wiring 304 connect between the mobile turf instrumentapparatus 282 and computing device 300 to transfer data from the mobileturf instrument apparatus 282 to the computing device 300 where the datais stored in memory.

Mobile turf instrument apparatus 282 is coupled to data collectionvehicle 280 and operates to take measurements of the turf 103 as it ismoved across the surface of the turf 103. An example of a mobile turfinstrument apparatus is described in U.S. Pat. No. 7,628,059 titledMOBILE TURF INSTRUMENT APPARATUS HAVING DRIVEN, PERIODICALLY INSERTABLE,GROUND PENETRATING PROBE ASSEMBLY, issued on Dec. 8, 2009.

In the example illustrated in FIG. 4 , mobile turf instrument apparatus282 includes a wheeled frame 310, elongated revolving arm 312, a soilmoisture sensor package 314, ground penetrating probes 316, weights 318,global positioning system (GPS) device 320, spectrometer 322, compactionpenetrometer 324, and salinity sensor 326.

Elongated revolving arm 312 is coupled to wheeled frame 310, andpositioned within an open central space of wheeled frame 310. Theelongated revolving arm 312 is revolved by a drive taken from one wheelof wheeled frame 310 to move elongated revolving arm 312 in a revolvingmotion.

Ground penetrating probes 316 extend from a lower end of elongatedrevolving arm 312 toward turf 103 (shown in FIG. 2 ). Weights 318 arecoupled to the elongated revolving arm. When the elongated revolving arm312 reaches a forward position, the elongated revolving arm is releasedand the weights 318 apply a force to the ground penetrating probes 316to cause them to be inserted into turf 103. The elongated revolving arm312 is allowed to pivot as the data collection vehicle 280 and mobileturf instrument apparatus 282 continue to move forward, to allow theground penetrating probes 316 to remain in turf 103 for a period oftime.

While the ground penetrating probes 316 are in the ground, the soilmoisture sensor package 314 generates electrical signals at the groundpenetrating probes 316. One or more measurements are then taken toevaluate a quality of the turf at that location, such as to determinethe volumetric water content of the turf 103. In some embodiments,salinity sensor 326 is also coupled to ground penetrating probes 316,which generates signals to determine the salinity of soil of turf 103 atthat location. Some embodiments further include a temperature sensor formeasuring canopy temperature, or a compaction sensor (e.g., a compactionpenetrometer or load cell) for determining the compaction of the soil atthat location.

Some embodiments further include a spectrometer 322. In someembodiments, spectrometer 322 is a near infrared spectrometer sensor. Insome embodiments the spectrometer 322 operates to measure the amount ofphotosynthetically active energy (red and blue light) absorbed by theturf canopy as an indication of photosynthesis and plant vigor. Thespectrometer 322, in some embodiments, emits energy in the red andnear-infrared portions of the spectrum, and measures the amountreflected by the canopy. The spectrometer 322 then calculates anormalized ratio of the two called Normalized Difference VegetativeIndex (NDVI). In some embodiments the NDVI varies from 0 to 1, where thehigher the NDVI value, the more vigorous the turf. The NDVI measurementis responsive to physiological changes in turf plants caused by factorssuch as moisture stress, nutrient deficiency or disease or insect damagewhich are frequently short-term in nature. Since photosynthesis is anatural process, its intensity can change with time of day andmicroclimatic conditions which can in turn affect NDVI values. NDVI isalso responsive to turf canopy architecture and turf density, whichoften reflect long-term or chronic conditions affecting turfperformance. Although not separately described in more detail herein,the spectrometer data can be processed similar to the water contentdata, or other collected data as described herein.

GPS device 320 determines GPS coordinates for each location where datais collected. The GPS coordinates are included with each datameasurement that is collected so that each measurement is associatedwith the location where the measurement was made.

FIG. 5 is a flow chart illustrating an example method 350 of evaluatinga turf site. In this example, method 350 includes operations 352, 354,356, 358, and 360. Other embodiments include more or fewer operations.

Method 350 begins with operation 352 that collects data from a turf site101. In an example embodiment, data collection is performed by mobiledata collection device 140, shown in FIG. 4 . In other embodiments,other data collection devices can be used. During operation 352, one ormore qualities of turf 103 (shown in FIG. 2 ) is evaluated. For example,one or more of the following are collected: normalized differencevegetation index (an indication of turf vigor), compaction (measured asthe force required to insert a probe into the soil), volumetric watercontent percentage (such as measured by time-domain reflectometry orcapacitance), soil salinity (such as using the Turf Guard™ systemavailable from The Toro Company, or a Wenner array), and location (suchas using GPS device to measure latitude, longitude, and altitude). Anexample of operation 352 is illustrated and described in more detailwith reference to FIGS. 6-7 .

A variety of alternative data collection devices can be used in otherembodiments, such as a hand-held instrument, or instruments mounted on aframe or cart that are pushed or pulled along the turf site 101, orother devices suitable for collecting data about the turf site 101.

Operation 354 is then performed to send the data collected in operation352 to the data processing lab 142, shown in FIG. 1 . The data can betransferred using any of a variety of data communication techniques,such as by transferring the data across the Internet, storing the dataon a computer-readable medium (such as a flash drive, CD-ROM, externalhard disk drive, or other computer-readable medium) and mailing ordelivering the computer-readable medium to the data processing lab 142,etc.

Once the data has been received by the data processing lab 142,operation 356 is performed to process the data. A variety of dataprocessing operations can be performed. Some example data processingoperations are illustrated and described herein with reference to FIGS.8-21 . As one example, the data is processed to identify boundaries of aplurality of irrigation management units. One or more qualities can thenbe determined for each irrigation management unit. As another example,the data is processed to identify a plurality of irrigation managementzones, where each zone includes one or more of the irrigation managementunits. Each irrigation management unit within an irrigation managementzone is determined to have one or more qualities that are similar to theother irrigation management units in the irrigation management zone.

Operation 358 is then performed to generate results based on the dataprocessing. One example of a result is the identification of irrigationmanagement zones. Another example of a result is the identification ofcontrol parameters that define how the irrigation system should operatewith respect to each irrigation management zone. Another example of aresult is the generation of color coded maps that visually illustratethe results that have been obtained. Another example of a result is theidentification of locations for in-ground moisture sensors. A furtherexample of a result is the identification of physical changes that couldbe made to the irrigation system to improve the performance of theirrigation system. For example, the results may identify pairs ofsprinkler heads that should be decoupled to permit the irrigation systemto control the sprinkler heads differently. Other results can also beobtained.

Operation 360 is then performed to provide the results to permit theirrigation system to be adjusted. The results can be provided, forexample, to the golf course superintendent, who can evaluate the resultsand make the desired adjustments. In another possible embodiment, theresults are provided directly to the irrigation control system 120, suchas by transferring the results across the Internet or delivering theresults to the irrigation control system 120 on a computer-readablemedium. The results can be, for example, in the form of controlparameters, program data, or application programs or plug-ins. Theresults modify the operation of the irrigation control system 120.Examples of operation 360 are illustrated in FIG. 1 .

FIG. 6 is a schematic diagram illustrating the collection of data fromturf site 101. The process of collecting data shown in FIG. 6 is anexample of operation 352, shown in FIG. 5 .

In this example, turf site 101 includes a fairway 105 which is coveredin turf 103. Sprinkler heads 132 are distributed around the fairway 105to provide water to turf 103.

In this example embodiment, the mobile data collection device 140performs several operations to collect data relating to fairway 105. Theoperations can be performed in any desired order. In one example,however, the operations are performed as follows. First, the mobile datacollection device 140 is driven along the boundary of fairway 105 toidentify the edges of the turf 103 that is going to be evaluated.Second, the locations of sprinkler heads 132 are determined by drivingacross each sprinkler head 132, and logging the location of eachsprinkler head 132. Third, a data collection sweep of the turf 103 isperformed to collect data about the turf 103 within the boundaries ofthe boundary pass.

Each operation will now be described in more detail. The boundary passis performed to identify boundary 384 of the fairway 105. In someembodiments, only GPS device 320, shown in FIG. 4 , is used to collectGPS data during the boundary pass, and other instruments can be turnedoff or temporarily disabled. The boundary pass can be completed quicklybecause the mobile data collection device 140 does not need to performsoil sampling. The mobile data collection device 140 begins the boundarypass at starting point 382 and collects GPS data as it moves alongboundary 384. The GPS data includes, for example, latitude, longitude,altitude, speed, and time data. The boundary pass continues until themobile data collection device arrives at the end point 382, which is thesame as the starting point of the boundary 384. The boundary locationdata is stored with the computing device 300, shown in FIG. 4 .

Locations of sprinkler heads 132 are determined by driving the mobiledata collection device 140 to the location of each sprinkler head 132.When the mobile data collection device 140 arrives at a sprinkler head132, the GPS unit is positioned directly above the sprinkler head 132and the GPS coordinate is logged. For example, an input is provided tothe computing device 300 on the data collection vehicle 280 (both shownin FIG. 4 ) to indicate that a sprinkler head has been located. Thesprinkler head location data is then stored with the computing device300.

A turf sweep is performed to collect the individual data points relatingto turf 103 located within the boundary 384. In this example, the turfsweep began at starting point 386 and proceeded along a plurality ofpasses 388, 390, 392, and continued 394 until all data has beencollected. Each pass is typically spaced approximately evenly fromadjacent passes. For example, pass 388 may be spaced approximately 6-10feet from pass 390, and pass 390 may be spaced approximately 6-10 feetfrom pass 392, etc. In some embodiments, each pass is about 8-10 feetapart. Wider or narrower passes can be used if desired.

During the turf sweep, the GPS device 320 continues to collect locationdata to identify the location where each data point is collected. Inaddition, at least one other instrument or device operates to measure aquality of the turf 103, such as the water content, soil salinity,color, and soil compaction. Data points are typically spacedapproximately evenly along each pass 388, 390, 392, etc. For example, insome embodiments a data point is taken about every 6-10 feet, orapproximately every 8 feet.

A larger spacing between data points and between passes can be used toreduce the number of data points collected, such as to increase thespeed of data collection, to reduce the amount of data to be processed,to reduce the number of holes formed in turf 103 by the groundpenetrating probes, or to reduce compaction caused by the mobile datacollection device 140. Larger spacing may be suitable if qualities ofthe turf tend to vary gradually along the turf.

A smaller spacing between data points and between passes can be used toincrease the number of data points collected, such as to obtain finerresolution of data points. The finer resolution may be desirable if theturf 103 tends to have more drastic variations, such as caused by largechanges in steepness or aspect, or widely varying soil conditions.Smaller spacing can also be desirable for smaller turf areas, such asgreens.

The turf sweep continues until the entire region within boundary 384 hasbeen sampled. In some embodiments, turf site 101 includes multiplesections, in which case the process is then repeated for each section.For example, turf site 101 can include 9, 18, or more fairways which areall managed by irrigation system 102. Accordingly, data can be collectedfrom all of the fairways using the same or similar process until theentire turf site 101 of interest has been sampled. The data is thenstored, such as in computing device 300, shown in FIG. 4 .

The timing of when a data collection operation is performed will dependon the goal of the irrigation system evaluation. If the goal of theevaluation is to determine the inherent properties of the soil and turfat the turf site 101, the data collection operation should be performedshortly after a heavy rain, which provides a substantially uniformamount of water to the turf site 101. In this state, turf site 101 issaid to be at field capacity. The data that is collected when turf site101 is at or near field capacity, will reflect the natural variations inthe turf site 101. For example, differences in soil type will influencemoisture readings. Soil that is primarily sand will tend to permit thewater to pass through quickly, resulting in a lower moisture contentmeasurement, while soil that is high in clay and organic matter willtend to hold the moisture, resulting in a higher moisture contentmeasurement. As another example, an area of the turf site 101 that has asteep slope will tend to allow the water to run off of that area beforethe water can soak into the turf. Therefore the turf in that area willtend to have a lower moisture content measurement than areas which areflat.

In contrast, if the goal of the evaluation is to evaluate theperformance of the irrigation system, the data collection operationshould be performed after a period in which little or no rain hasfallen. In this case, the moisture in the soil will largely reflect theperformance of the irrigation system 102.

Further, in some embodiments the data collection operation is performedmultiple times, such as once after a heavy rain, and again after a dryspell. The data can then be compared to provide even more informationabout the turf site 101 and irrigation system 102.

FIG. 7 graphically illustrates the data collected in operation 352,shown in FIG. 5 , and the process shown in FIG. 6 . In the exampledescribed in FIG. 6 , a fairway is sampled and data is collectedregarding the turf 103 located within the boundary 384 of the fairway.The data includes boundary location data 402, sprinkler head locationdata 404, and data points 406.

Once the data is obtained, it is stored in a data file, such as in acomma separated values (CSV) format. Other file formats can be used,such as spreadsheet file format, a table format, or a binary fileformat.

In some embodiments, all of the collected data includes location data,including latitude and a longitude where the data was collected.Accordingly the data can be graphically depicted as shown in FIG. 7 ,where the data is shown at the location where it was collected.

The data can include, for example, boundary location data 402 whichidentifies the location of boundary 384 of the fairway 105. The data canalso include, for example, sprinkler head location data 404, whichidentifies the location of each sprinkler head 132 associated withfairway 105. The data can further include, for example, data points 406,which identify at least one quality of the turf at the location of thedata point 406.

As shown in FIG. 5 , the data is then sent to data processing lab 142,such as in operation 354.

FIG. 8 is a schematic block diagram of an example data processing lab142 that receives the data collected by the mobile data collectiondevice 140. In this example, data processing lab 142 includes anirrigation management zoning module 420 and a fine tuning module 422.

In one example embodiment, data processing lab 142 receives thecollected data and processes the data to identify irrigation managementzones. Each irrigation management zone is a region (or multiple regions)of the turf site 101 that have similar qualities, such as similar needsfor watering. In some embodiments, the irrigation management zones areinitially determined based, at least in part, on the water content datapoints that were collected for the turf site 101. An example of theirrigation management zoning module 420 is illustrated and described inmore detail with reference to FIG. 9 .

Some embodiments further include a fine tuning module 422. The measuredwater content for a turf site 101 only provides some of the dataregarding the needs of the turf in given regions of turf site 101.Additional factors can also be important to evaluate, and can result inthe need to modify the bounds of the irrigation management zonesidentified by the irrigation management zoning module. Examples of suchfactors include soil salinity and topography. These factors areevaluated by the fine tuning module 422, which utilizes additional datafrom the collected data. The results of the data processing performed bythe fine tuning module can be used to modify the irrigation managementzones, or to otherwise adjust the results that are then output from thedata processing lab 142. An example of the fine tuning module 422 isillustrated and described in more detail with reference to FIG. 16 .

Data processing lab 142 typically includes at least one computingdevice, such as the computing device illustrated in FIG. 3 , whichperforms at least some of the data processing operations of dataprocessing lab 142. In some embodiments the data processing lab 142 isfully automated, such that all operations are performed by one or morecomputing devices. In another embodiment, some of the operations areperformed by, or involve the interaction of, a human. In someembodiments the data processing lab 142 includes software that, whenexecuted by the computing device, performs one or more of the operationsdescribed herein.

FIG. 9 is a schematic block diagram of an example irrigation managementzoning module 420. In this example, irrigation management zoning module420 includes interpolation engine 430, irrigation management unit engine432, irrigation management zoning engine 434, and graphical mappingengine 436.

Interpolation engine 430 operates to improve the resolution of datapoints, such as to convert the collected data points 406, shown in FIG.7 , to fine data points 450, such as shown in FIG. 10 . In someembodiments the initial data collection operates to measure qualities ofthe turf 103 at spaced intervals. For example, the data points can becollected in a grid pattern, where each data point is spacedapproximately eight to ten feet from adjacent data points. Theinterpolation engine 430 processes the data to identify fine datapoints, which are estimated values at locations between known datapoints. An example of the interpolation engine 430 is described in moredetail with reference to FIG. 10 .

Irrigation management unit engine 432 operates to segment the turf site101 into a plurality of irrigation management units. In someembodiments, an irrigation management unit is the smallest region of theturf site 101 for which the irrigation system 102 can control. In atypical irrigation system 102, the smallest region that the irrigationsystem 102 can control is defined by a single sprinkler head. The areaimmediately surrounding each sprinkler head defines a single irrigationmanagement unit, which is primarily controlled by that sprinkler head.The irrigation management unit engine 432 operates to identify each ofthe irrigation management units, and to define the boundaries of eachunit. An example of the irrigation management unit engine 432 isillustrated and described in more detail with reference to FIGS. 12-13 .

Irrigation management zoning engine 434 operates to group irrigationmanagement units, identified by the irrigation management unit engine432, into irrigation management zones. For example, the irrigationmanagement zoning engine 434 identifies irrigation management unitshaving similar characteristics, and groups those units into a singleirrigation management zone. In some embodiments, the irrigation system102 can be programmed to control all of the sprinkler heads within anirrigation management zone the same, due to the similar characteristicsof the turf within that zone. An example of the irrigation managementzoning engine is illustrated and described in more detail with referenceto FIGS. 14-15 .

Graphical mapping engine 436 operates to graphically display data on amap of the turf site 101. The graphical display shows the results of thedata processing in a format that is easy to understand by the user oranother human, such as the golf course superintendent. The graphicaldisplays can be printed or saved in an electronic format to be includedwith the results provided back to the person in charge of the turf site101, such as the superintendent. In some embodiments, graphical mappingengine 436 utilizes the Google Earth program provided by Google Inc. Forexample, satellite imagery can be displayed with Google Earth, and dataoverlays can then be generated to graphically depict data over thesatellite imagery. Several examples of graphical displays are shown inFIGS. 7, 10-13, 15, and 17 .

FIG. 10 is a graphical representation of interpolated data calculated bythe interpolation engine 430, shown in FIG. 9 , based on data collectedfrom a turf site 101. Only a portion of the interpolated data isillustrated in FIG. 10 , as indicated by the broken line 449.

The graphical representation illustrates a fairway 105 of a turf site101, having a boundary 384. Spaced apart data points 406 were collectedfrom the turf site 101. The data points 406 are interpolated to generatefine data points 450 which are associated with locations that are closertogether than the locations associated with collected data points 406,resulting in higher resolution data. For example, if the collected datapoints 406 are taken at eight foot by eight foot intervals, the fineresolution data points can be generated for smaller intervals, such astwo foot by two foot intervals. Other intervals can be used, such as onefoot or smaller intervals, or larger intervals.

A variety of interpolation techniques can be used in variousembodiments, such as linear interpolation, polynomial interpolation,spline interpolation, etc. Some embodiments utilize geostatisticalinterpolation techniques, such as kriging. Kriging assumes a spatialrelationship between the data, and therefore compares data points withadjacent data points when computing fine data points 450.

Because data points 406 are only available at locations within boundary384, the boundary data 402 (shown in FIG. 7 ) can be used to identifyboundaries of the interpolation. The interpolation computes fine datapoints 450 for all locations within boundary 384.

In some embodiments, fine data points 450 are computed for all locationsthat fall at intersections of a spaced grid. For example, a two foot bytwo foot grid is defined by the interpolation engine. Fine data points450 are then computed for each intersection of the grid. Theintersections may not align with all of the collected data points 406,and so collected data points 406 may not be included in fine data point450, unless they do properly align.

In some embodiments, collected data points include various qualities ofthe turf, such as water content, soil salinity, vigor, etc. The datapoints can describe In such embodiments, interpolation can be performedfor each type of data. For example, fine data points are computed forthe water content by interpolating the water content data points.Similarly, fine data points are computed for soil salinity byinterpolating the salinity data points. Similar interpolation can beperformed for each type of data.

FIG. 11 is a graphical representation of the interpolated data for asingle quality of turf 103 for fairway 105 of turf site 101. Thegraphical representation is generated, for example, by graphical mappingengine 436, which displays a graphical representation of fairway 105.

In this example, water content data has been interpolated to obtain finedata points. Each fine data point has a location (such as latitude andlongitude) and a value (such as water content). Based on the fine datapoints, graphical mapping engine 436 can generate a graphicalrepresentation of the data. The graphical representation displays thevalues using colors that are associated with the value, and the colorsare positioned at the appropriate location for each data point. Thisgraphical representation displays the data in a way that is easy tounderstand by a human.

FIGS. 12-13 illustrate an example of the operation of the irrigationmanagement unit engine 432, shown in FIG. 9 .

FIG. 12 is a graphical representation of operations performed byirrigation management unit engine 432 to define boundaries of irrigationmanagement units. In this example, the irrigation management unit engine432 begins by identifying the locations of all of the sprinkler heads,such as at points 470, 472, 474, 476, etc.

After the location of each sprinkler head has been determined, theirrigation management unit determines the boundaries of a region of turfsite 101, where all points within the region are closer to therespective sprinkler head than any other sprinkler head. The identifiedregion of space for each sprinkler head is referred to as an irrigationmanagement unit.

One method of identifying the boundaries of the irrigation managementunits is as follows. After the sprinkler head locations have beenidentified, midpoints 480, 482, 484, 486, 488, 490, etc. between eachsprinkler head locations 470, 472, 474, and 476 and each adjacentsprinkler head are located. For example, a line can be drawn betweensprinkler head locations 470 and 472. The midpoint 480 is thenidentified. The same is performed between each adjacent sprinkler head.

Once the midpoints have been identified, a perpendicular bisector isdrawn through each midpoint. This line is perpendicular to the linethrough the two sprinkler heads, and crosses through the midpoint. Theperpendicular bisectors are extended until they meet another bisector,resulting in the boundaries shown in FIG. 13 .

FIG. 13 is a graphical representation of additional operations performedby irrigation management unit engine 432 to define boundaries ofirrigation management units.

Boundaries of the irrigation management units are defined by theperpendicular bisectors described with reference to FIG. 12 . Theperpendicular bisectors form polygons that define the boundaries of eachof the irrigation management units. In this example, twelve irrigationmanagement units are identified, because there are twelve sprinklerheads in fairway 105. The irrigation management units include units 502,504, 506, and 508. Irrigation management unit 502 is bounded by bisector510, which is the perpendicular bisector of a line crossing throughsprinkler heads locations 470 and 476. Because the other ends ofirrigation management unit 502 are not bounded by the process identifiedabove (i.e., intersection of the perpendicular bisector with anotherperpendicular bisector), outer boundaries can be defined. In someembodiments the outer boundary is located at boundary 384 of the fairway105, based on boundary data 402 (shown in FIG. 7 ). However, boundariesof fairways, such as fairway 105 can change over time. In order toaccommodate for future variations in boundary 384, the outer bounds orirrigation management units can be defined as a distance away from therespective sprinkler head. For example, the outer boundaries 520 and 522may be located twenty or thirty feet away from the sprinkler head 470.Because no data has been collected for points lying outside of boundary384, the regions outside of boundary 384 will not be included insubsequent evaluation of the irrigation management zone (unless or untilsuch data is gathered).

Once the irrigation management units 502, 504, 506, 508, etc. have beenidentified, further processing can be performed for each unit. Forexample, general qualities of each unit can be identified, and a numberrepresenting the quality can be assigned to the unit. Any measuredquality, or quality that can be computed from measured qualities, can beevaluated.

For example, in some embodiments each irrigation management unit 502,504, 506, 508, etc. is evaluated to determine an average water content(WC) of that unit. To compute the average moisture content forirrigation management unit 502, the irrigation management unit engine432 identifies all fine data points 450 (shown in FIG. that are locatedwithin the boundaries of irrigation management unit 502. The estimatedvolumetric water content values are then identified for each fine datapoint 450, and an average of these values is computed. The resultingvalue (e.g., WC12) is saved as the average water content value forirrigation management unit 502. The same process is repeated for eachirrigation management unit (504, 506, 508, etc.) until all irrigationmanagement units have been assigned a water content value (WC1-WC11).

In some embodiments, an average value is a mean value. In otherembodiments, an average value is a median value. In yet otherembodiments, an average value is a value between, and not including, anextreme low value and an extreme high value. Other embodiments utilizeother statistics or algorithms to compute the average value. In someembodiments an average value is about an average value.

Similarly, other qualities can be evaluated for each irrigationmanagement unit. For example, soil salinity, soil compaction, vigor,etc. can be used to compute an average value, which is then assigned tothe irrigation management unit as an indication of the average of thatquality across the irrigation management unit. An example of thedetermination of average salinity values is illustrated and describedwith reference to FIG. 17 .

In some embodiments, the average values are displayed on a map of theturf site 101 using graphical mapping engine 436 (shown in FIG. 9 ),such as in a display similar to that shown in FIG. 13 , but alsoincluding the average values. In further embodiments each irrigationmanagement unit is color coded based on the value for that unit. Forexample, the colors can vary from a light color to a dark colordepending on the value. In another possible embodiment, colors areassigned to ranges of values. Any irrigation management unit having avalue within a first range of values is displayed in a first color. Anyirrigation management unit having a value within a second range ofvalues is displayed in a second color, etc.

FIGS. 14-15 illustrate an example of the irrigation management zoningengine 434, shown in FIG. 9 .

FIG. 14 is a diagram illustrating the classification of irrigationmanagement units into irrigation management zones. FIG. 15 is agraphical representation of the irrigation management zones.

After identifying irrigation management units and average values ofvarious qualities of the turf in each irrigation management unit, theirrigation management zoning engine performs operations to classify theirrigation management units into multiple irrigation management zones,where each of the irrigation management units that are included within asingle irrigation management zone have similar characteristics.

One example method of classifying irrigation management units intoirrigation management zones is to compare the average water contentvalues for each irrigation management unit.

FIG. 14 is a plot of water content values for each irrigation managementunit verses the number of irrigation management units that have thatvalue. Next, the irrigation management units are classified into anumber of irrigation management zones. In one example embodiment, thenumber of irrigation management zones is between four and six zones.Other embodiments include other numbers of zones.

It is generally preferred to keep the number of zones as low aspossible, while still providing an advantage of the classification intozones. A benefit of classifying the units into common zones is thatfewer control parameters need to be defined for the entire turf site101. For example, the turf site may be divided into ten, twenty, or ahundred or more different irrigation management units. While eachirrigation management unit could be separately controlled, it wouldrequire that control parameters be identified for each unit. Byclassifying the irrigation management units into a smaller set ofirrigation management zones, many fewer control parameters need to beidentified, and the superintendent can more easily manage each zone.

The number of irrigation management zones should be less than the totalnumber of irrigation management units, such that at least some of thezones include two or more irrigation management units. On the otherhand, the number of irrigation management zones should be large enoughthat most or all of the different sections of the turf site 101 (such aseach fairway), have at least two zones—unless a given section is highlyuniform.

In this example, it has been determined that the irrigation managementunits (including those in fairway 105, as well as all other sections ofturf site 101) will be classified into four irrigation management zones.The classification is performed based on the average water contentvalues for each unit, such that the irrigation management units havingthe lowest values are assigned to irrigation management zone 540, thenext irrigation management units are assigned to irrigation managementzone 542, the next irrigation management units are assigned toirrigation management zone 544, and the irrigation management units withthe highest water content are assigned to irrigation management zone546.

Various techniques can be used to determine where to separate irrigationmanagement units between adjacent zones. For example, if an irrigationmanagement unit has a value of 30, whether the unit should be placedinto zone 540 or zone 542. One method of classifying the zones is todetermine the total number of irrigation management units, and dividethat number by the number of irrigation management zones. Then, thelowest number of units is assigned to zone 540, etc.

Another embodiment utilizes an algorithm to compute the locations ofnatural breaks between zones. For example, the Jenks Natural BreaksClassification method can be used to identify natural breaks in the datato assign the irrigation management units to the appropriate zones. Someembodiments of the irrigation management zoning engine 434 utilize theNatural Breaks tool of the ArcView geographic information systemdistributed by Environmental Systems Research Institute, Inc. ofRedlands, CA.

FIG. 15 illustrates the classification of irrigation management unitsinto irrigation management zones for a section (fairway 105) of the turfsite 101. In this example, the irrigation management units have beenclassified into irrigation management zones 542, 544, and 546. Forexample, irrigation management zone 546 includes irrigation managementunits 502, 506, and 508.

In some embodiments, once the irrigation management units have beenassigned an irrigation management zone, each irrigation management zonecan be assigned an average value, such as an average water content valuebased on the average water content values of the irrigation managementunits included in that zone. Based on this value, the irrigationmanagement zoning module 420 can determine appropriate controlparameters for the irrigation control system. For example, based on thewater content values, the amount of water that needs to be applied tothe turf in the irrigation management zone can be identified. In someembodiments the quantity of water is based on a duration of time thatthe sprinkler valves should be opened. In some embodiments, the controlparameters are a function of an in-ground moisture sensor signal. Someembodiments utilize an evapotranspiration (ET) sensor. For example, thecontrol parameter can identify an amount of time to turn on thesprinkler once the in-ground water sensor signal identifies a watercontent below a threshold value. An example of an in-ground moisturesensor is the Turf Guard™ soil sensor available from The Toro Company.The system may include further rules, such as to only water duringcertain hours of each day.

Other control parameters can also be assigned. For example, in someembodiments the frequency of application is assigned. The frequencyrelates to the number of on-off cycles that should be used for a singleapplication. Stated another way, the frequency relates to the number ofrest periods that are included in a single watering session, includingthe duration and quantity of the rest periods. For example, if water isbeing applied to an area with a steep slope, it may be advantageous toapply the water in several shorter applications, with a rest periodin-between that permits the water time to soak into the turf. As anotherexample, multiple on-off cycles may be advantageous in areas where thesoil is compacted. In some embodiments the frequency of application isset to a default value, such as for a single application. The defaultvalue can then be adjusted, such as by the fine tuning module.

In some embodiments, at least one in-ground moisture sensor is installedin each irrigation management zone. In some embodiments, only a singlesensor is used for each zone. Because all irrigation management unitswithin the zone have been determined to have similar characteristics, asingle in-ground moisture sensor can be installed in an “average”location (such as by referring to one or more of the graphical datarepresentations described herein). This reduces the number of in-groundmoisture sensors that are required in the turf site 101, while providingthe information needed to efficiently operate the irrigation system 102.

In some embodiments, one or more soil samples are collected from eachirrigation management unit, or each irrigation management zone. The soilsample is analyzed to determine the soil texture classification, or toidentify other characteristics of the soil. The data can then be used,for example, to control the irrigation system accordingly.

FIG. 16 is a schematic block diagram of fine tuning module 422 of thedata processing lab 142, shown in FIG. 8 . In this example, fine tuningmodule 422 includes salinity processing engine 560, topographyprocessing engine 562, and graphical mapping engine 564.

In some embodiments, irrigation management zones are at least initiallydetermined based on water content. However, it is recognized themoisture content alone does not give a complete picture of the moisturerequirements of a given irrigation management unit. Rather, there areadditional factors that can influence the amount of water that needs tobe provided to an irrigation management unit, and the way in which thewater should be delivered. Examples of these factors include salinityand topography.

Salinity processing engine 560 performs data processing to evaluate thesalinity of irrigation management units of turf site 101. Salinity isthe salt content in the turf 103 of turf site 101. The primary source ofsalt is the water from the irrigation system 102. If water applied tothe turf 103 does not soak through the turf 103, the water can evaporateor be consumed by the turf 103, while the salt content of the water isleft behind to accumulate in the turf 103. Excess salt can bedetrimental to the health of the turf 103. To remove excess salt, aleaching process can be performed to provide an excess of water(referred to as the leaching fraction) to the turf 103, to flush thesalt from the turf 103. The leaching fraction is the amount of waterthat must be applied to the turf 103, in excess of the amount to beconsumed by the turf 103, to cause a flushing of the salt from the turf103.

In some embodiments, the salinity processing engine 560 determines theaverage salinity of the turf 103 for each irrigation management unit.Once the salinity is known, the salinity processing engine 560 can beutilized to adjust the irrigation management zones, or to modify thecontrol parameters for the irrigation management zones. In someembodiments, salinity processing engine 560 generates control parametersfor operating the irrigation system 102 in a leaching mode. An exampleof salinity processing engine 560 is illustrated and described withreference to FIG. 17 .

Topography processing engine 562 operates to evaluate the topography ofthe turf site 101, to determine how the topography might influenceirrigation needs for the irrigation management units. In someembodiments the topography processing engine 562 evaluates both thesteepness of the turf 103 in each irrigation management unit, and alsothe aspect of the turf 103. The aspect of the turf is the direction inwhich the turf 103 is facing at a given point, such as North, South,East, or West. In some embodiments, the aspect is computed as a degree,where North is 0 degrees, East is 90 degrees, South is 180 degrees, andWest is 270 degrees.

Graphical mapping engine 564 is provided to generate graphicalrepresentations of data, for presentation of the data to a human.

FIG. 17 is a graphical representation of operations performed bysalinity processing engine 560 to evaluate the salinity of the soil atturf site 101. The salinity processing engine utilizes the sameirrigation management units 502, 504, 506, 508, etc. generated by theirrigation management zoning module 420 (shown in FIG. 9 ), as well asthe salinity data collected for the turf site in operation 352 (shown inFIG. 5 ) and interpolated by the interpolation engine 430 (shown in FIG.9 ).

In some embodiments, the salinity processing engine 560 computes averagesalinity values (S) for each irrigation management unit, in a similarmanner that the water content values can be computed for each irrigationmanagement unit (as discussed herein with reference to FIG. 13 ). Allsalinity values (either fine data points 450, shown in FIG. 10 , orcollected data points 460, shown in FIG. 7 , or both) within anirrigation management unit are identified, and the average of thosevalues is computed (S). The average value (S) is then assigned as theaverage value (S) for the irrigation management unit. For example,irrigation management unit 502 is assigned an average salinity value ofS12. The process is repeated to identify average salinity values(S1-S11) for each irrigation management unit of the turf site 101.

Once salinity values have been determined, a graphical representation ofthe values can be generated by graphical mapping engine 564 (shown inFIG. 16 ). The graphical representation can be similar to the exampleshown in FIG. 17 , and can further include the values (S1-S12), ifdesired. In another possible embodiment, each irrigation management unitis displayed in a color associated with the value. For example, thecolor can be a range of colors from a light color to a dark colordepending on the salinity value. In another possible embodiment, anyirrigation management unit having values within a first range of valuesare displayed with a first color. Any irrigation management unit havingvalues within a second range of values are displayed with a secondcolor, etc. The graphical representations of the salinity values permita human to evaluate the salinity values and determine whetheradjustments should be made to the preliminary irrigation managementzones, or whether adjustments should be made to control parameters ofirrigation system 102.

In some embodiments, salinity zones are defined based on the salinityvalues (S1-S12) for each irrigation management unit. Salinity zones canbe defined in the same way that irrigation management zones are defined,as discussed herein. Once salinity zones have been defined, theirrigation system 102 can be programmed to operate in a leaching mode.When in the leaching mode, a leaching fraction of water can be providedin addition to the normal water needs of the zone, to flush the saltfrom the turf 103. Because the leaching fraction is applied only tothose irrigation management units or zones with high salt content, wateris saved. More specifically, the leaching fraction is not supplied toirrigation management units or zones that do not have a high saltcontent, thereby reducing the amount of water used during the leachingoperation as compared with the application of water to the entire turfsite 101 during a leaching operation.

In some embodiments, salinity data is used to at least modify irrigationsystem control parameters. During a watering cycle, water content datamay indicate that a certain quantity of water should be applied to theirrigation management units within an irrigation management zone. Thesalinity data can be used to increase the amount of water to be suppliedto irrigation management units or zones so as to provide an additionalleaching fraction. In some embodiments in-ground salinity sensors areused to monitor the salinity levels in the turf 103, and to initiate theleaching mode when the salinity levels reach a threshold level.

FIG. 18 is a schematic block diagram of the topography processing engine562. In some embodiments, topography processing engine 562 is part ofthe fine tuning engine 422, shown in FIG. 16 , which is part of the dataprocessing lab 142, shown in FIG. 8 . In this example, topographyprocessing engine 562 includes steepness engine 602 and aspect engine604.

The topography processing engine 562 is used to evaluate the topographyof the turf site 101, such as to identify possible adjustments that canbe made to the irrigation management zones, or to the control parametersof the irrigation system 102. In some embodiments the topographyprocessing engine 562 evaluates at least two aspects of the topography,including the steepness of the turf 103 and the aspect of the turf 103.Steepness and aspect are both characteristics of the slope of the turf103.

Steepness engine 602 operates to evaluate the steepness of the turf 103of the turf site 101. In some embodiments, the steepness engine 602evaluates the GPS data to determine localized steepness values. The datais then averaged across each irrigation management unit to generate asteepness score. The steepness score for each irrigation management unitis then stored in memory, and can be used to display a graphicrepresentation of the steepness, such as using the graphical mappingengine 564, shown in FIG. 16 . The steepness can then be used to adjustthe frequency at which water is supplied to the turf. For example, ifthe turf in an irrigation management unit is relatively flat, it may besuitable to apply the water in a single sustained application. On theother hand, if the turf in an irrigation management unit has a steepslope, a sustained application of water may result in water running offof the slope without soaking into the turf. Therefore, it may bepreferable to apply the water to an area with a steep slope in severalshorter intervals with periods of time in-between to permit the watertime to soak into the turf. An example of the steepness engine 602 isillustrated and described herein with reference to FIG. 19 .

Aspect engine 604 operates to evaluate the horizontal direction that theturf faces, such as North, South, East, or West. Aspect can influencethe amount of moisture that is required by the turf. For example, in thenorthern hemisphere, south facing surfaces tend to receive more directsunlight than northern facing surfaces. Therefore, the moisturerequirement of the turf in a southern facing irrigation management unitwill be greater than turf facing other directions. The aspect engineevaluates the aspect of the turf in each irrigation management unit, anddetermines an overall aspect score for each irrigation management unit,which is saved in memory. The aspect score can then be used to adjustcontrol parameters of the irrigation system 102, for example. An exampleof the aspect engine 604 is illustrated and described with reference toFIG. 19 .

FIG. 19 is a chart illustrating the operation of the steepness engine602, shown in FIG. 18 . In some embodiments, the steepness engine 602operates to evaluate the slope steepness of turf in turf site 101, andto assign average steepness values to each irrigation management unit ofturf site 101.

steepness engine 602 receives as an input GPS data, which is part of thecollected data of operation 352, shown in FIG. 5 . Most GPS units do notmeasure steepness values directly, and therefore steepness engine 602must perform processing steps on GPS data to generate steepness values.Steepness values can be computed from the latitude, longitude, andaltitude values, where the steepness is equal to the rise (difference inaltitude) over the run (different in distance based on latitude andlongitude). It has been found that the altitude values that are recordedby a typical GPS system are not, themselves, highly reliable. However,differences between two collected and adjacent altitude values tend tohave greater reliability. Further, these values can be interpolated,such as using the Kriging technique described elsewhere herein, todefine spatial relationships between the altitude difference values (inother words, to compare data points with other surrounding data points)to provide even more reliable data. The steepness engine 602 utilizesthis process to compute fine data points for all parts of the turf site101.

Once the fine data points have been computed, the steepness engine 602identifies all data points without the boundaries of a given irrigationmanagement unit, and computes an average steepness value for thatirrigation management unit. The process is repeated for all irrigationmanagement units, and the average steepness values are stored in memory.

In some embodiments, the steepness engine 602 assigns a steepness scoreto each irrigation management unit. The steepness score is assigned, forexample, as shown in FIG. 19 . In this example, the average steepnessvalues are classified into six different categories, ranging from flat,to moderate steepness, to a steep slope. A flat slope (e.g., less than2.6 degrees) is given a first steepness score, such as 0. A steep slope(e.g., greater than 12 degrees) is given a sixth steepness score, suchas 50. Slopes between flat and steep are given appropriate second,third, fourth, and fifth scores, such as 10, 20, 30, and 40respectively. The steepness scores for each irrigation management unitare then stored in memory.

The steepness scores (or average steepness values) can be used by thegraphical mapping engine 564 to generate a graphical representation ofthe slope steepnesses on a site map. For example, irrigation managementunits having a score of 0 are given a first color. Scores of 10, 20, 30,40, and 50 are given different colors. The graphical representation isuseful to a human to visualize the steepness data, such as to identifydesired adjustments to the irrigation system 102, or modifications tocontrol parameters of the irrigation control system 120.

FIG. 20 is a diagram illustrating the operation of the aspect engine604, shown in FIG. 18 . In some embodiments, the aspect engine 604 is aportion of the topography processing engine 562, which is a portion ofthe fine tuning module 422, shown in FIG. 16 , which is a portion of thedata processing lab 142, shown in FIG. 8 .

The aspect engine 604 operates to evaluate the direction that the turf103 of turf site 101 faces, such as to assign an aspect value and anaspect score to each irrigation management unit.

The data computed by the steepness engine 602 can be utilized by aspectengine 604. In some embodiments the aspect engine 604 begins bycomputing aspect directions for each data point. The aspect directionis, for example, the horizontal component of a line normal to the turfsurface. The aspect values can be computed using the latitude,longitude, and aspect values, such as by comparing adjacent values toeach other. Interpolation can be used to obtain fine data points, asdiscussed herein.

Once the aspect data points have been computed, an overall aspect valueis identified for each irrigation management unit, as shown in FIG. 20 .Each aspect value data point is an angle, where 0° represents due North,90° represents due East, 180° represents due South, and 270° representsdue West. The aspect engine 604 generates aspect scores based on theaspect value, as shown in FIG. 20 . In this example, the scores arebased on a turf site located in the northern hemisphere. For turf sitesin the southern hemisphere, the values for North and South may bereversed. In addition, for turf sites located at or about the equator,different aspect scores may be appropriate. In this example, an aspectvalue that is North facing (between about 315° and 45°), the data pointis assigned an aspect score of 1. A data point having an aspect valuethat is East facing (between about 45° and 135° is assigned an aspectscore of 2. A data point having an aspect value that is West facing(between about 135° and 225°) is assigned an aspect score of 3. A datapoint having an aspect value that is South facing (between about 225°and 315°) is assigned an aspect score of 4. Other embodiments includeother scoring techniques. For example, in some embodiments other anglesare used, such as to limit the definition east and west facing slopes toa smaller range of directions (e.g., East is between 67.5 and 112.5,etc.). Different aspect scores can be assigned to each direction inother embodiments. In yet another embodiment, more than four directionsare used, such as including Northwest, Northeast, Southwest, andSoutheast directions, and aspect scores are assigned accordingly.

An example is illustrated in FIG. 20 , in which a single data point hasan aspect value (A) of 100°. Because 100° is between 45° and 135°, thedata point is assigned an aspect score of 2, representing an East facingslope.

Once all of the data points have been assigned an aspect score, thequantity of data points having a given score are summed for eachirrigation management unit. For example, there may be 10,269 data pointsthat have an aspect score of 1, 2,940 data points that have an aspectscore of 2, etc. The aspect engine then determines which aspect scorehas the greatest number of data points. This aspect score is thenselected and assigned as the aspect score for the irrigation managementunit. The process is repeated for each irrigation management unit, andthe resulting aspect scores are stored in memory.

The graphical mapping engine 564, shown in FIG. 16 , can be used todisplay the aspect data on a map of the turf site 101, to permit visualevaluation of the results.

FIG. 21 is a diagram illustrating operations of the topographyprocessing engine 562, shown in FIG. 18 , to generate a final topographyscore for each irrigation management unit.

Once the steepness and aspect scores have been generated by thesteepness engine 602 and the aspect engine 604, shown in FIG. 18 , afinal topography score is generated by the topography processing engine562 for each irrigation management unit. In some embodiments thetopography score is the sum of the steepness score and the aspect score.

One example of a topography scoring algorithm is illustrated in FIG. 21, where the steepness is represented by concentric rings and the aspectis represented by angles from 0° to 360°. For example, the outerconcentric ring represents irrigation management units having a flatslope that have been assigned a steepness score of zero. Steeper slopes(with steepness scores of 10 to 50) are represented by the innerconcentric circles, where the innermost concentric circle representsirrigation management units having a steepness score of 50. The aspectscore of each irrigation management unit is also shown. For example, anirrigation management unit having an aspect score of 0 is shown in thetop of the chart (North), an aspect score of 1 is shown on the right ofthe chart (East), an aspect score of 2 is shown on the left of the chart(West), and an aspect score of 4 is shown on the bottom of the chart(South).

Once the steepness and aspect scores are identified for an irrigationmanagement unit, the appropriate topography score can be assigned to theirrigation management unit.

The topography processing engine 562 retrieves the steepness and aspectscores for each irrigation management unit from memory, and utilizes thescores to identify the topography score. For example, an irrigationmanagement unit having a flat topography (steepness score of 0 and anyaspect score) is assigned a topography score of 0. An irrigationmanagement unit having a steepness score of 20 and an aspect score of 1is assigned a topography score of 21. Other scores are assigned as shownin FIG. 21 . However, in other possible embodiments other scoringalgorithms can be used.

Once the topography scores have been defined, the topography scores canbe used by the graphical mapping engine 564 (shown in FIG. 16 ) togenerate graphical representations of the data on a map of the turf site101. For example, the topography scores can be displayed for eachirrigation management unit, or each irrigation management unit can becolor coded according to the topography score. In some embodiments, eachaspect is assigned a color, such as blue for north, green for east,yellow for west, and red for south. The shade of the color is thendetermined by the steepness score, such as light for flat slopes anddark for steep slopes. Other color schemes are used in otherembodiments.

In addition, in some embodiments the topography scores are used tomodify control parameters for the irrigation control system 120. Forexample, the topography score can be used to adjust the frequency ofwater delivery, where irrigation management units (or zones) having ahigher score can be watered using a greater frequency of water delivery(e.g., five applications of three minutes each) to permit the water timeto soak in between applications. Irrigation management units (or zones)having a lower score can be watered with less frequency (e.g., a singleapplication for fifteen minutes).

The topography score can also be used to adjust the volume of water tobe applied to an irrigation management unit (or zone). For example, thecontrol parameters for an irrigation management unit having a topographyscore that ends in 4 can be increased to provide more water to theirrigation management unit, while a control parameter for an irrigationmanagement unit having a topography score of 0 can be used withoutfurther adjustment.

Similarly, topography scores can be used to suggest modifications thatshould be made to the irrigation system. For example, if two irrigationmanagement units are within the same irrigation management zone, but oneof the units has a topography score of 40, while the other unit has atopography score of 0, it may be beneficial to separate the irrigationmanagement units into separate zones—such as by assigning one of theunits to a different irrigation management zone.

As another example, if the sprinkler heads of two irrigation managementunits are currently hardwired together, or otherwise commonlycontrolled, different topography scores for each unit may suggest thatthe irrigation management units should be decoupled from each other topermit the irrigation management units to be separately managed.

Topography scores can also be used to determine a good location for anin-ground moisture sensor. The ideal location for an in-ground moisturesensor is in a location of the irrigation management zone that has“average” qualities as compared with other locations within that zone.Therefore, the topography scores can be used to identify an irrigationmanagement unit that has a score that is roughly at a midpoint betweenthe other scores. The in-ground moisture sensor can then be inserted inthat irrigation management zone.

Referring now back to FIG. 1 , after the data processing of the dataprocessing lab 142 has been completed, the results can be transferred tothe turf site 101. For example, physical adjustments can be made to theirrigation system 102 to improve the performance of the irrigationsystem. Such modifications may include, for example, the installation ormoving of in-ground moisture sensors, the installation or moving ofsprinkler heads, the re-wiring of already installed sprinkler heads, thereplacement of sprinkler heads that were found to be underperforming, orother physical modifications. The data processing lab 142 can alsogenerate control parameters that can be programmed into the irrigationcontrol system 120 to modify the operation of the irrigation system. Inanother possible embodiment, the data is presented in a printed orelectronic form as a report, and the report is presented to thesuperintendent or manager of the turf site 101. The superintendent ormanager may then review the data and make whatever adjustments he or shedeems appropriate based on the data in the report.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the claimsattached hereto. Those skilled in the art will readily recognize variousmodifications and changes that may be made without following the exampleembodiments and applications illustrated and described herein, andwithout departing from the true spirit and scope of the followingclaims.

1-21. (canceled)
 22. A system for watering a turf site, the systemcomprising: a processing device; and a memory device storing computerreadable instructions executable by the processing device to cause theprocessing device to: control a first plurality of sprinkler headswithin a first irrigation management zone according to a first set ofcontrol parameters, the first irrigation management zone including oneor more irrigation management units having a first commoncharacteristic; control a second plurality of sprinkler heads within asecond irrigation management zone according to a second set of controlparameters, the second irrigation management zone including one or moreirrigation management units having a second common characteristic;receive measurements of at least one parameter of the irrigationmanagement units in the first and second irrigation management zones;and adjust the first and second sets of control parameters based on themeasurements of the at least one parameter of the irrigation managementunits in the first and second irrigation management zones.
 23. Thesystem of claim 22, further comprising: the first and second pluralitiesof sprinkler heads; and water lines connected to a source of water andto valves that control the flow of water through the first and secondpluralities of sprinkler heads.
 24. The system of claim 22, furthercomprising: a sensor for measuring the at least one parameter.
 25. Thesystem of claim 22, wherein each irrigation management unit includes asensor for measuring the at least one parameter.
 26. The system of claim22, wherein the measurements of the at least one parameter includesalinity measurements of the irrigation management units.
 27. The systemof claim 22, wherein the measurements of the at least one parameterinclude moisture measurements of the irrigation management units. 28.The system of claim 22, wherein the measurements of the at least oneparameter include temperature measurements of the irrigation managementunits.
 29. The system of claim 22, wherein the measurements of the atleast one parameter include compaction measurements of the irrigationmanagement units.
 30. The system of claim 22, wherein adjust the firstand second sets of control parameters includes adjusting a frequency atwhich water is supplied to the irrigation management units in the firstand second irrigation management zones.
 31. The system of claim 22,wherein adjust the first and second sets of control parameters includesadjusting a duration at which water is supplied to the irrigationmanagement units in the first and second irrigation management zones.32. The system of claim 22, wherein the first set of control parametersdiffers from the second set of control parameters based on a differenceof the first common characteristic from the second commoncharacteristic.
 33. A method of watering a turf site, the methodcomprising: controlling a first plurality of sprinkler heads within afirst irrigation management zone according to a first set of controlparameters, the first irrigation management zone including one or moreirrigation management units having a first common characteristic;controlling a second plurality of sprinkler heads within a secondirrigation management zone according to a second set of controlparameters, the second irrigation management zone including one or moreirrigation management units having a second common characteristic;receiving measurements of at least one parameter of the irrigationmanagement units; and adjusting the first and second sets of controlparameters based on the measurements of the at least one parameter ofthe irrigation management units in the first and second irrigationmanagement zones.
 34. The method of claim 33, wherein the measurementsof the at least one parameter include salinity measurements of theirrigation management units.
 35. The method of claim 33, wherein themeasurements of the at least one parameter include moisture measurementsof the irrigation management units.
 36. The method of claim 33, whereinthe measurements of the at least one parameter include temperaturemeasurements of the irrigation management units.
 37. The method of claim33, wherein the measurements of the at least one parameter includecompaction measurements of the irrigation management units.
 38. Themethod of claim 33, wherein adjusting the first and second sets ofcontrol parameters includes adjusting a frequency at which water issupplied to the irrigation management units in the first and secondirrigation management zones.
 39. The method of claim 33, whereinadjusting the first and second sets of control parameters includesadjusting a duration at which water is supplied to the irrigationmanagement units in the first and second irrigation management zones.40. A non-transitory computer readable storage device storing datainstructions for controlling an irrigation system, the instructions,when executed by a computing device, cause the irrigation system to:control a first plurality of sprinkler heads within a first irrigationmanagement zone according to a first set of control parameters, thefirst irrigation management zone including one or more irrigationmanagement units having a first common characteristic; control a secondplurality of sprinkler heads within a second irrigation management zoneaccording to a second set of control parameters, the second irrigationmanagement zone including one or more irrigation management units havinga second common characteristic; receive measurements of at least oneparameter of the irrigation management units in the first and secondirrigation management zones; and adjust the first and second sets ofcontrol parameters based on the measurements of the at least oneparameter of the irrigation management units in the first and secondirrigation management zones.
 41. The non-transitory computer readablestorage device of claim 40, wherein adjust the first and second sets ofcontrol parameters includes adjusting a frequency, a duration, or boththe frequency and the duration at which water is supplied to theirrigation management units in the first and second irrigationmanagement zones.